Detection of SARS-CoV-2 by Canine Olfaction: A Pilot Study.

Background: As the number of coronavirus disease 2019 (COVID-19) cases continue to surge worldwide and new variants emerge, additional accurate, rapid, and noninvasive screening methods to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed. The number of COVID-19 cases reported globally is >455 million, and deaths have surpassed 6 million. Current diagnostic methods are expensive, invasive, and produce delayed results. While COVID-19 vaccinations are proven to help slow the spread of infection and prevent serious illness, they are not equitably available worldwide. Almost 40% of the world's population remains unvaccinated. Evidence suggests that SARS-CoV-2 virus-associated volatile organic compounds found in the breath, urine, and sweat of infected individuals can be detected by canine olfaction. Medical detection dogs may be a feasible, accurate, and affordable SARS-CoV-2 screening method. Methods: In this double-blinded, case-control, validation study, we obtained sweat samples from inpatients and outpatients tested for SARS-CoV-2 by a polymerase chain reaction test. Medical detection dogs were trained to distinguish SARS-CoV-2-positive samples from SARS-CoV-2-negative samples using reward-based reinforcement. Results: Samples were obtained from 584 individuals (6-97 years of age; 24% positive SARS-CoV-2 samples and 76% negative SARS-CoV-2 samples). In the testing phase, all dogs performed with high accuracy in detecting SARS-CoV-2. The overall diagnostic sensitivity was 98%, and specificity was 92%. In a follow-up phase, 1 dog screened 153 patients for SARS-CoV-2 in a hospital setting with 96% diagnostic sensitivity and 100% specificity. Conclusions: Canine olfaction is an accurate and feasible method for diagnosis of SARS-CoV-2, including asymptomatic and presymptomatic infected individuals.

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study.

BACKGROUND: A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. METHODS: Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. RESULTS: About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95-100) to 100% and specificity from 99% (95% CI 97-100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76-87) to 94% (95% CI 89-98) and specificity ranging from 76% (95% CI 70-82) to 92% (95% CI 88-96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. CONCLUSIONS: People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people.Trial Registration NCT04509713 (clinicaltrials.gov).

Artificial Neural Networks for classifying the time series sensor data generated by medical detection dogs

The aim of this research was to discover if artificial neural networks can be used to classify pressure sensor data generated by medical detection dogs as they sniff biological samples. A detection dog can be trained to recognise the odour emitted by one of a wide range of diseases such as prostate cancer, malaria or, potentially, COVID-19. The dog searches a row of sample pots and indicates a positive sample by sitting in front of it. This offers a non-invasive means of diagnosing the specific cancer or disease that the dog has been trained to recognise. For this study, pressure sensors were attached to the sample pots to generate time series data pertaining to the dog’s searching behaviour as they press their nose against the sample pot to sniff its content. Automatic classification could provide a second form of indication, to support or refute the dog’s explicit signal (to sit at a positive sample), which is not always correct. Ultimately, classification software could eliminate the need for the dog to perform an indication gesture, making the dog’s task easier and training quicker. Four different neural network architectures were evaluated: multilayer perceptron (MLP), a convolutional neural network (CNN), a fully convolutional network (FCN) and ResNet (a deep convolutional neural network). Each model was trained to classify the pressure data generated by medical detection dogs. To achieve a useful level of accuracy, it was found that the models needed to be trained using only those data samples where the dog had correctly classified the scent sample. Model hyperparameters were tuned to improve accuracy. We found that the best performing model was MLP. When tested on previously unseen data, where the dog was not always correct, the classification performance of the MLP approached that of the medical detection dogs. For our particular dataset, the model’s true positive rate (i.e. recall) was 59%, matching that of the dogs. The model’s true negative rate was 79%, compared to the dogs’ 91%.